Devops Engineer – Ai-Driven Deployments (Remote)

Fullstack; Municipio de Rincón de los Sauces; Remote; ARS 400,000 - 650,000; Full time

Job summary

Fullstack is seeking a Senior Machine Learning Ops Engineer to own architecture, development, and automation of end-to-end ML solutions. You will bridge data science and software engineering, evaluating cloud options, and deploying scalable models into production.

This hands-on role emphasizes technical health of the ML lifecycle, cost-efficient cloud architectures, and delivering high-quality systems that drive business value in a global client environment.

Qualifications

• Bachelor’s degree in software engineering/computer science or proven equivalent experience in data engineering or ML roles.
• Hands-on experience implementing and deploying machine learning solutions in production environments.
• Advanced knowledge of Python, SQL, and Spark.

Responsibilities

• ML Architecture & Development: Architect and develop robust, end‑to‑end machine learning solutions.
• MLOps & Automation: Manage and automate the complete machine learning lifecycle, ensuring seamless transitions from development to production.
• Cross‑Functional Collaboration: Collaborate closely with data engineers and data scientists to create highly scalable solutions and integrate them with other technical areas across the software development life cycle.
• Cloud & Architecture Strategy: Work on cloud solutions, actively evaluating the performance and cost‑efficiency of potential architectures.
• Business Alignment: Interact with other teams and stakeholders to deeply understand business challenges and propose effective technical solutions.
• Mentorship & Advocacy: Communicate technical concepts clearly and teach teams how to properly use and adopt the new developments.

Skills

Python

SQL

Spark

Education

Bachelor’s degree in software engineering / computer science

Job description

Coderio designs and delivers scalable digital solutions for global companies. We combine strong technical expertise with a product mindset to lead complex software initiatives end-to-end. We work with international clients, value autonomy and clear communication, and build long‑term partnerships through technical excellence. More information:

This is a high‑impact technical role. We are looking for a Senior Machine Learning Ops Engineer to join our team. You will own the architecture, development, and automation of end‑to‑end machine learning solutions. Operating within an established engineering team, you will be a key player in bridging the gap between data science and software engineering, evaluating cloud solutions, and deploying scalable machine learning models into production.

This is a hands‑on execution role. You are responsible for the technical health of the ML lifecycle, the performance and cost‑efficiency of the cloud architecture, and delivering high‑quality, scalable solutions that drive business value.

What To Expect In This Role (Responsibilities)

ML Architecture & Development: Architect and develop robust, end‑to‑end machine learning solutions.

MLOps & Automation: Manage and automate the complete machine learning lifecycle, ensuring seamless transitions from development to production.

Cross‑Functional Collaboration: Collaborate closely with data engineers and data scientists to create highly scalable solutions and integrate them with other technical areas across the software development life cycle.

Cloud & Architecture Strategy: Work on cloud solutions, actively evaluating the performance and cost‑efficiency of potential architectures.

Business Alignment: Interact with other teams and stakeholders to deeply understand business challenges and propose effective technical solutions.

Mentorship & Advocacy: Communicate technical concepts clearly and teach teams how to properly use and adopt the new developments.

Requirements

Educational Foundation: Bachelor’s degree in software engineering/computer science, or proven equivalent experience in data engineering or machine learning roles.

ML Deployment Mastery: Proven hands‑on experience implementing and deploying machine learning solutions in production environments.

Core Stack Expertise: Advanced knowledge of Python, SQL, and Spark.